1. Field of the Invention
The present invention is directed to a method for use with a stream of images defining a video wherein the method includes periodically conducting a face finding operation on an image in the stream of videos and in respect of the last image in the stream preceding the image in which one or more faces are found, using a tracker based upon wavelet decomposition to find a face for each face found in the last image for which no counterpart was found.
2. Prior Art
Face detection plays a crucial role in a wide range of applications such as human computer interface, facial biometrics, video surveillance, gaming, video analytics and face image database management. Often, these real world applications rely heavily on the face detection as the first stage of the overall system. Typically face detection algorithms are built with one or more assumptions, such as, frontal face, illumination conditions and no occlusions. Consequently, these algorithms become quite unreliable when dealing with real world difficult scenarios. One such application area is “fitness to drive” where, sudden changes in the driver's face pose, illumination, reflections as well as occlusions cannot be avoided.
Most of the existing face detection algorithms address only few of these challenging scenarios. Consequently, many databases have been made public where one of the problems is the reoccurring theme in their respective database; the YALE database is the most common database used for variations in illumination. Variations in illumination proves to be an excessive challenge for researchers as there are infinite possibilities of lighting variations that can occur in real world scenarios. For example, lighting variations can range from variable light source location to multiple light sources. The HONDA database strictly focuses on different face orientations. Face orientation continues to be a challenge as face detection for a partially occluded face often becomes difficult as common classifiers typically rely on specific key features on the face.
The proposed algorithm attempts to tackle all three of the problems mentioned above simultaneously by applying preprocessing to the target frame to normalize the amount of illumination variation, applying a cascading set of classifiers to increase the overall range of face orientation the algorithm is confidently able to detect and applying an adaptive Discrete Wavelet Transform (DWT) based tracker to track the location of the face if the cascading classifiers fail.